中国科学院心理健康重点实验室知识库

AimsTo predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy.MethodsSixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups.ResultsThe proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital alpha band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal theta band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838.ConclusionOur results revealed that, compared to nonresponders, VNS responders had a more efficient alpha band brain network, especially in the parieto-occipital region, and less spectral complexity of theta brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model. The long-term efficacy of VNS was assessed among 65 pediatric patients. Presurgical EEG analysis shows that VNS responders exhibit higher nodal efficiency in parietal-occipital EEG alpha activity and lower entropy in central-frontal EEG theta activity. The SVM model with clinical and EEG features for VNS efficacy shows high accuracy.image